Work

Soil erosion susceptibility modeling using stacking ensemble machine learning, Kangra District, north-western Himalaya, India

Year: 2026

Type: article

Abstract: The present study aims to predict soil erosion-prone regions in Kangra District using ML algorithms; Support Vector Machine (SVM), Random Forest (RF), XGBoost and CatBoost and their stacking ensembles. Additionally, the study also utilizes the SHAP (SHapley Additive exPlanations) model to enhance the interpretability of the best-performing RF algorithm. For this, a comprehensive soil erosion point dataset was developed using empirical field survey integrated with visual interpretation of Google Earth historical images. The results show RF as the best-performing model, predicting 14.7% of the area into the very high susceptibility zone, followed by XGBoost (27.1%), SVM (24.4%) and CatBoost (16.4%), whereas the stacking ensemble predicted 25.2%. The SHAP analysis of the best-performing model (RF) indicates NDVI, slope and LULC as the primary governing factors influencing soil erosion in the region. This study also provides a scientific base for policy formulation and strategic planning to mitigate soil erosion in Kangra District.

Source: Geocarto International

DOI: 10.1080/10106049.2026.2737528

Authors: Ranjeet Verma, Amrita Dwivedi, Swati Sharma, Harsimran Kaur

Institutions: Indian Institute of Technology BHU, Banaras Hindu University

Language: English

Topic: Soil erosion and sediment transport

Subfield: Soil Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $2,390